
Designing AI localization workflows for global media teams
The translation took seconds. Checking it took minutes. I spent an internship on the unglamorous half of an AI product: not generating the output, but making it cheap to trust.
The situation
Two panels. Reviewers kept losing their place between them.
I was brought in on Vosyn's internal translation screen. The real problem was reviewing its output.
Nobody complained about translation quality. They complained about finding out whether it was good.
The reframe
The unit is the check. Not the translation itself.
Timing reviewers on real files showed fast readers doing the same read three times, re-finding their place.
Before: the original and its translation open one at a time, so the translation is judged against a memory of the original. After: both side by side, with the literally translated idiom flagged.
Review tool
One view, both texts. The system points at what needs a human.
Each flag names a failure a reviewer can judge in one read, not a confidence score they can't check.
Video Q&A
Answers cite sources. Nothing arrives unattributed.
Ask about a stretch of video, and the answer names the segment it came from.
A grounded answer can be disagreed with. An ungrounded one can only be believed.
Reflection
Generating is half. Deciding is the other half.
Small changes to workflow structure beat new features. Reducing the cost of verification is a product strategy, not a polish task.
- Mine
- Interaction design
- UI design
- Prototyping
- Workflow analysis
- Shared
- User researchwith UX researcher
- Product scopewith PM
- Nearby screenswith 2 designers
- Not mine
- Formal usability testing
- Translation models
- Platform architecture
Numbers are from usability testing, not production telemetry. Flags only catch failure modes we defined; they don't make an unreviewed file safe.
- Role
- Product Designer
- Team
- Product Designer (Me), 2 Designers, UX Researcher, Product Manager, 2 Developers
- Timeline
- Aug–Dec 2024
- Tools
- Figma (FigJam + Design), Balsamiq, Storybook, UserTesting